In some instance, images include regions with missing content, such as an area in the image where pixel values are incorrect, thereby creating a “hole” in the image. Conventional techniques used to predict content (e.g., color, texture, and so on) with which to fill the hole and restore the image generally generate an initial prediction of each pixel value in the hole by copying its nearest pixel value in the image, such as pixel values immediately surrounding the hole. These initial pixel values are then used for a similarity search within a larger search region in the image using a similarity search algorithm to refine the initial prediction.
These conventional techniques, however, can introduce visible errors and incorrect content in the hole based on the initial pixel values, particularly when the surrounding pixel values are non-uniform. This initiation error cannot be corrected using the conventional techniques. Because of these limitations, some holes cannot be filled appropriately and thus, some images cannot be restored correctly using conventional techniques.